Learn about Machine Learning frameworks with NVIDIA
Developers and fanatics enthusiastic about studying extra about Machine Learning frameworks could also be enthusiastic about a brand new framework interoperability sequence created by the staff at NVIDIA. In the primary half you’ll study about distinct reminiscence layouts and the way the Apache Arrow format can considerably velocity up knowledge transfers throughout distinct knowledge science and machine studying frameworks reminiscent of TensorFlow, PyTorch, MXNet, cuDF, CuPy, Numba, and JAX. As properly as how asynchronous reminiscence allocation facilitated by reminiscence swimming pools is essential to keep away from overheads as massive as 90% of the general runtime of your pipeline.
“Efficient pipeline design is crucial for data scientists. When composing complex end-to-end workflows, you may choose from a wide variety of building blocks, each of them specialized for a dedicated task. Unfortunately, repeatedly converting between data formats is an error-prone and performance-degrading endeavor. Let’s change that!”
In this Machine Learning frameworks sequence, NVIDIA talk about completely different points of environment friendly framework interoperability:
- We begin with this submit discussing execs and cons of distinct reminiscence layouts in addition to reminiscence swimming pools for asynchronous reminiscence allocation to allow zero-copy performance.
- In the second submit, we spotlight bottlenecks occurring throughout knowledge loading/transfers and easy methods to mitigate them utilizing Remote Direct Memory Access (RDMA) expertise.
- In the third submit, we dive into the implementation of an end-to-end pipeline demonstrating the mentioned methods for optimum knowledge switch throughout knowledge science frameworks.
“Zero-copy functionality is a crucial technique to efficiently copy data across GPU-accelerated data science frameworks: TensorFlow, PyTorch, MXNet, cuDF, CuPy, Numba, and JAX (see Figure 2). In the following, we will show you how to achieve that in a systematic manner. If you are only here to look up the commands on how to transfer data from one framework to another, you might want to have a look at this conversion table.”
Source : NVIDIA
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